The operations of process plants have been leveraged by the availability of analytical methods, for example, through the use of laboratory measurements or online analyzers. These types of results are valued by plant operations because they are typically regarded as reliable. For example, with respect to hydrocarbon and/or refining operations, primary test methods (PTMs) provide a critical basis for custody transfer of products whose properties have been ascertained in accordance with industry standard test methods such as those developed and promulgated by ASTM International.
Notwithstanding the importance of these PTMs, they do suffer from certain inadequacies. For example, laboratory measurements typically may be available only once or several times per day. Furthermore, several hours can elapse between the obtaining of a discrete sample and the reporting of results from tests performed on it, severely limiting the possibility to control the process on the basis of those results. Additionally, sample stability, sample contamination, issues of representative sampling, and uncertainty associated with the execution of test procedures may result in erroneous sample values being returned.
Improving the availability, integrity, and reproducibility of test data has in many cases motivated the on-line deployment of measurements. Yet, depending upon the type of measurement and analysis being performed, the cycle time for online analyzers may be on the order of ten or more minutes and, in some cases, up to and over one hour, which, in some cases, may still be inadequate for purposes of maximizing process efficiency or product quality.
Process industries have conventionally responded to the time delay issues and reliability of primary measurements by instituting secondary measurement techniques capable of predicting properties of certain process streams. Such secondary techniques commonly have included the use of some form of model, for example, multivariate statistical models capable of predicting certain properties of interest using process inputs, in which the properties of interest may be termed “dependent variables” and the process inputs may be termed “independent variables.”
An important class of these model-based approaches is “inferential analyzers,” also referred to as “soft sensors” because they typically reside in software. Soft sensors are appealing for at least two reasons. First, many times they do not require the installation of additional sensors in the process unit because they typically rely upon measurements such as temperature, pressure, and flow rate, which may already be available. Second, with the advent of distributed control systems, the input measurements typically relied upon by soft sensors are substantially available in real-time, having discrete sampling rates of one second or even less. These advantages at least address the disadvantage of time delay associated with primary measurements by providing property predictions at intervals that are greater than those typically required by process control systems. Additionally, they obviate the need to physically obtain a sample, eliminating the issues of representative sampling and sample integrity.
While the fidelity of these models may be quite good over limited time periods ranging from a few hours to even perhaps a few days, conventional inferential analyzers tend to be insufficiently robust because in aggregate the independent variables that serve as inputs into the model typically relate to the chemistry of the process stream both indirectly and incompletely. They are indirect expressions of the chemistry to the extent that the readings of sensors on the process are functions of both process conditions and material in the process; they are incomplete insofar as the number of independent variables used in the models is fewer than the degrees of freedom in the system, which relate to both the process system and the material being processed through it. But an exception may occur when steady-state or quasi-steady-state conditions prevail and many process and stream variables are nominally constant, e.g. when feed quality and the operation of the process system are substantially invariant. At such times, the independent variables may “determine” stream chemistry in the mathematical sense, and property predictions by an inferential analyzer may be extremely reliable. Yet, a fundamental issue is that models generally are correlative, and because correlation does not necessarily denote cause, inferential models may be largely empirical, with first principles having only distant influence. Indeed, the literature freely refers to the modeling approach that is perhaps most common as a “black box method.” In summary, property predictions by inferential analyzers are labile to the extent that the effect (a predicted value) is removed from the primary cause (a stream property that ultimately is determined by sample chemistry).
The preceding, general discussion finds specific relevance where the PTM is realized with a “hard” analyzer that measures the vapor pressure of a hydrocarbon sample; the sensor is a spectrometer based on a molecular spectroscopy technique; and the soft (inferential) analyzer is a model applied to the spectrum of the sample measured by the spectrometer. The common practice is to periodically update the model to overcome its inaccuracies resulting from a variety of variables including but not limited to changes in (i) the range of possible feed compositions and properties; (ii) the proportions of feed streams being combined into the product being monitored; and (iii) product property specifications. Yet, model updates are after the fact, typically being done when vapor pressure predictions are shown to be inaccurate. The corollary is that in any given moment, the validity an inferential prediction may be quite uncertain, and may in fact lack the required accuracy.
Even small inaccuracies in knowledge of vapor pressure in hydrocarbon streams can significantly impact the economies of production for large-scale processes similar to those process units found in petroleum refineries and other hydrocarbon processing operations. The need therefore exists in the art for improved methods for achieving and validating vapor pressure measurement with a high degree of accuracy, preferably on-line in substantially real-time, despite the aforementioned changes in operating conditions that can undermine the reliability of current methods, both hard and soft.